Performance of Low-rise Reinforced Concrete Buildings in the 2016 Ecuador Earthquake

Sim, Chungwook; Villalobos, Enrique; Smith, Jhon Paul; Rojas, Pedro; Pujol, Santiago; Puranam, Aishwarya Y.; Laughery, Lucas

doi:10.4231/r7zc8111

Cited by 2 publications

(1 citation statement)

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“…The study presented in [50] incorporates cumulative data collected from RC buildings shaken under the past four seismic events in Ecuador, Haiti, Nepal, and South Korea. The data are archived in the open-access database of the DataCenterHub platform [51], simultaneously with the data obtained from the studies by different research groups on that platform [52][53][54][55]. Data from the individual earthquakes were not sufficient for predictive models to work efficiently.…”

Section: Input Data Sourcementioning

confidence: 99%

Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings

et al. 2022

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The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency.

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Section: Input Data Sourcementioning

confidence: 99%